A PLURIPOTENT STEM CELL PLATFORM FOR in VITRO SYSTEMS GENETICS STUDIES of MOUSE DEVELOPMENT
Ontology highlight
ABSTRACT: The directed differentiation of pluripotent stem cells (PSCs) from panels of genetically diverse individuals is emerging as a powerful experimental system for understanding genotype-phenotype relationships and modeling polygenic traits and disorders. Here, we establish a genetic reference panel of naïve and primed PSCs (n = 250) from the Diversity Outbred (DO) mouse stock to enable high resolution quantitative trait locus mapping of molecular phenotypes in developing cellular systems. These data, methods, and cellular resources provide a roadmap for population-scale studies to dissect the genetic basis of complex traits that will complement and empower similar studies in human populations.
Project description:Linkage analysis of complex traits in mice is a powerful tool to find loci affecting the phenotype but it has a poor resolution making it difficult to identify the underlying genes. We show here, using whole genome association analysis of gene expression traits in an outbred mouse population, the MF1 stock, that mapping resolution is greatly increased as compared to linkage. The fact that eQTLs discovered in other crosses were replicated and successfully mapped with high resolution in this population provides a strong proof of concept. In addition, we show that this population is a useful resource to resolve the eQTL hotspots detected in other studies. Finally, we highlight the importance of correcting for population structure in whole genome association studies in the outbred stock. Keywords: genetic association study in outbred mice
Project description:Research into the genetic influences of impulsivity and reward motivated behavior relies heavily on outbred animal populations, including Heterogeneous Stock (HS) rats, for the genetic diversity necessary to identify genotype-trait associations. Many such associations have been detected, but it is not always clear which gene or other feature near the identified genomic location is functionally responsible for the association. Since these traits are in part mediated by gene expression, mapping the associations between genotype and gene expression in these animals will enable the discovery and deeper understanding of these trait associations. We therefore obtained genotypes and RNA-Seq gene expression for five brain regions from 88 HS rats and mapped expression quantitative trait loci (eQTLs) for each region. We identified cis-eQTLs in over 3,000 genes per brain region and validated their effect sizes using allele specific expression. This resource will enable new discoveries of the genetic influences of complex behavioral traits.
Project description:Research into the genetic influences of impulsivity and reward motivated behavior relies heavily on outbred animal populations, including Heterogeneous Stock (HS) rats, for the genetic diversity necessary to identify genotype-trait associations. Many such associations have been detected, but it is not always clear which gene or other feature near the identified genomic location is functionally responsible for the association. Since these traits are in part mediated by gene expression, mapping the associations between genotype and gene expression in these animals will enable the discovery and deeper understanding of these trait associations. We therefore obtained genotypes and RNA-Seq gene expression for five brain regions from 88 HS rats and mapped expression quantitative trait loci (eQTLs) for each region. We identified cis-eQTLs in over 3,000 genes per brain region and validated their effect sizes using allele specific expression. This resource will enable new discoveries of the genetic influences of complex behavioral traits.
Project description:Research into the genetic influences of impulsivity and reward motivated behavior relies heavily on outbred animal populations, including Heterogeneous Stock (HS) rats, for the genetic diversity necessary to identify genotype-trait associations. Many such associations have been detected, but it is not always clear which gene or other feature near the identified genomic location is functionally responsible for the association. Since these traits are in part mediated by gene expression, mapping the associations between genotype and gene expression in these animals will enable the discovery and deeper understanding of these trait associations. We therefore obtained genotypes and RNA-Seq gene expression for five brain regions from 88 HS rats and mapped expression quantitative trait loci (eQTLs) for each region. We identified cis-eQTLs in over 3,000 genes per brain region and validated their effect sizes using allele specific expression. This resource will enable new discoveries of the genetic influences of complex behavioral traits.
Project description:Research into the genetic influences of impulsivity and reward motivated behavior relies heavily on outbred animal populations, including Heterogeneous Stock (HS) rats, for the genetic diversity necessary to identify genotype-trait associations. Many such associations have been detected, but it is not always clear which gene or other feature near the identified genomic location is functionally responsible for the association. Since these traits are in part mediated by gene expression, mapping the associations between genotype and gene expression in these animals will enable the discovery and deeper understanding of these trait associations. We therefore obtained genotypes and RNA-Seq gene expression for five brain regions from 88 HS rats and mapped expression quantitative trait loci (eQTLs) for each region. We identified cis-eQTLs in over 3,000 genes per brain region and validated their effect sizes using allele specific expression. This resource will enable new discoveries of the genetic influences of complex behavioral traits.
Project description:Research into the genetic influences of impulsivity and reward motivated behavior relies heavily on outbred animal populations, including Heterogeneous Stock (HS) rats, for the genetic diversity necessary to identify genotype-trait associations. Many such associations have been detected, but it is not always clear which gene or other feature near the identified genomic location is functionally responsible for the association. Since these traits are in part mediated by gene expression, mapping the associations between genotype and gene expression in these animals will enable the discovery and deeper understanding of these trait associations. We therefore obtained genotypes and RNA-Seq gene expression for five brain regions from 88 HS rats and mapped expression quantitative trait loci (eQTLs) for each region. We identified cis-eQTLs in over 3,000 genes per brain region and validated their effect sizes using allele specific expression. This resource will enable new discoveries of the genetic influences of complex behavioral traits.
Project description:Genetic background is a major driver of the phenotypic variability observed across pluripotent stem cells (PSCs), and studies addressing it have relied on transcript abundance as the primary molecular readout of cell state. However, little is known about how proteins, the functional units in the cell, vary across genetically diverse PSCs and how this relates to variation in other measures of gene output. Here we present the first comprehensive genetic study characterizing the pluripotent proteome using 190 unique mouse embryonic stem cell lines derived from highly heterogeneous Diversity Outbred mice. Moreover, we integrated the proteome with chromatin accessibility and transcript abundance in 163 cell lines with matching genotypes using multi-omics factor analysis to distinguish shared and unique drivers of variability across molecular layers. Our findings highlight the power of multi-omics data integration in revealing the distal impacts of genetic variation. We show that limitations in mapping of individual molecular traits may be overcome by utilizing data integration to consolidate the influence of genetic signals shared across molecular traits and increase detection power.
2023-05-10 | PXD033001 | Pride
Project description:Gut microbiome genera associate with metabolic traits in outbred heterogeneous stock rats
Project description:Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWH/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.